Robotic multi-item type palletizing and depalletizing

ABSTRACT

Techniques are disclosed to use a robotic arm to palletize or depalletize diverse items. In various embodiments, data associated with a plurality of items to be stacked on or in a destination location is received. A plan to stack the items on or in the destination location is generated based at least in part on the received data. The plan is implemented at least in part by controlling a robotic arm of the robot to pick up the items and stack them on or in the receptacle according to the plan, including by for each item: using one or more first order sensors to move the item to a first approximation of a destination position for that item at the destination location; and using one or more second order sensors to snug the item into a final position.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/809,389 entitled ROBOTIC MULTI-ITEM TYPE PALLETIZING &DEPALLETIZING filed Feb. 22, 2019 which is incorporated herein byreference for all purposes.

BACKGROUND OF THE INVENTION

Shipping and distribution centers, warehouses, shipping docks, airfreight terminals, big box stores, and other activities that ship andreceive non-homogeneous sets of items use strategies such as packing andunpacking dissimilar items in boxes, crates, containers, conveyor belts,and on pallets, etc. Packing dissimilar items in boxes, crates, onpallets, etc. enables the resulting sets of items to be handled by heavylifting equipment, such as forklifts, cranes, etc., and enables items tobe packed more efficiently for storage (e.g., in a warehouse) and/orshipment (e.g., in truck, cargo hold, etc.).

In some contexts, items may be so dissimilar in size, weight, density,bulkiness, rigidity, strength of packaging, etc. that any given item orset of items may or may not have attributes that would enable thoseitems to support the size, weight, distribution of weight, etc., of agiven other item that might be required to be packed (e.g., in a box,container, pallet, etc. When assembling a pallet or other set ofdissimilar items, items must be selected and stacked carefully to ensurethe palletized stack does not collapse, lean, or otherwise becomeunstable (e.g., so as not to be able to be handled by equipment such asa forklift, etc.) and to avoid item damage.

Currently, pallets typically are stacked and/or unpacked by hand. Humanworkers select items to be stacked, e.g., based on a shipping invoice ormanifest, etc., and use human judgment and intuition to select largerand heavier items to place on the bottom, for example. However, in somecases, items simply arrive via a conveyor or other mechanism and/or areselected from bins in an order listed, etc., resulting in an unstablepalletized or otherwise packed set.

Use of robotics is made more challenging in many environments due to thevariety of items, variations the order, number, and mix of items to bepacked, on a given pallet for example, and a variety of types andlocation of container and/or feed mechanism from which items must bepicked up to be placed on the pallet or other container.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a diagram illustrating an embodiment of a robotic system topalletize and/or depalletize heterogeneous items.

FIG. 2 is a flow chart illustrating an embodiment of a process topalletize and/or depalletize.

FIG. 3A is a diagram illustrating an example of a pallet as assembled inan embodiment of a robotic system to palletize and/or depalletizeheterogeneous items.

FIG. 3B is a diagram illustrating an example of a pallet as assembled inan embodiment of a robotic system to palletize and/or depalletizeheterogeneous items.

FIG. 4A is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items.

FIG. 4B is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items.

FIG. 4C is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items.

FIG. 5 is a flow chart illustrating an embodiment of a process to placean item on a pallet.

FIG. 6 is a flow chart illustrating an embodiment of a process to placeitems on a pallet.

FIG. 7 is a flow chart illustrating an embodiment of a process to placean item on a pallet.

FIG. 8 is a flow chart illustrating an embodiment of a process todetermine a strategy to place an item on a pallet.

FIG. 9 is a block diagram illustrating an embodiment of a suction-basedend effector in a robotic system to palletize and/or depalletizeheterogeneous items.

FIG. 10 is a block diagram illustrating an embodiment of a gripper-styleend effector in a robotic system to palletize and/or depalletizeheterogeneous items.

FIG. 11 is a flow chart illustrating an embodiment of a process tocompliantly move an item to a destination location.

FIG. 12 is a flow chart illustrating an embodiment of a process tochange a robotic arm grasp of an item.

FIG. 13 is a flow chart illustrating an embodiment of a process to use arobotic arm to determine an attribute of an item to be stacked on apallet.

FIG. 14 is a flow chart illustrating an embodiment of a process to use arobotic arm to detect and respond to instability in a set of items asstacked on a pallet.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Techniques are disclosed to programmatically use a robotic systemcomprising one or more robots (e.g., robotic arm with suction, gripper,and/or other end effector at operative end) to palletize/depalletizeand/or to otherwise pack and/or unpack arbitrary sets of non-homogeneousitems (e.g., dissimilar size, shape, weight, weight distribution,rigidity, fragility, etc.

In various embodiments, 3D cameras, force sensors, and other sensors areused to detect and determine attributes of items to be picked and/orplaced. Items the type of which is determined (e.g., with sufficientconfidence, as indicated by a programmatically determined confidencescore, for example) may be grasped and placed using strategies derivedfrom an item type-specific model. Items that cannot be identified arepicked and placed using strategies not specific to a given item type.For example, a model that uses size, shape, and weight information maybe used.

In various embodiments, a library of item types, respective attributes,and grasp strategies is used to determine and implement a strategy topick and place each item. The library is dynamic in some embodiments.For example, the library may be augmented to add newly-encountered itemsand/or additional attributes learned about an item, such as graspstrategies that worked or did not work in a given context. In someembodiments, human intervention may be invoked if the robotic systemgets stuck. The robotic system may be configured to watch (e.g., usingsensors) and learn (e.g., updating a library and/or model) based on howa human teleoperator intervenes to use the robotic arm, for example, topick and place an item via teleoperation. In some embodiments,teleoperation may include higher-level forms of feedback, such as ahuman indicating a particular point in a 2D RGB image at which the robotshould grasp an object.

In some embodiments, a robotic system as disclosed herein may engage ina process to gather and store (e.g., add to library) attributes and/orstrategies to identify and pick/place an item of unknown ornewly-discovered type. For example, the system may hold the item atvarious angles and/or locations to enable 3D cameras and/or othersensors to generate sensor data to augment and/or create a library entrythat characterizes the item type and stores a model of how to identifyand pick/place items of that type.

In various embodiments, a high level plan to pick and place items on apallet or other container or location is made. Strategies are applied,e.g., based on sensor information, to implement the plan by picking andplacing individual items according to the plan. Deviations from the planand/or re-planning may be triggered, e.g., the robotic system getsstuck, the stack of items on the pallet is detected (e.g., by computervision, force sensors, etc.) to have become unstable), etc. A partialplan may be formed based on known information (e.g., next N itemsvisible on a conveyor, next N items on an invoice or manifest, etc.) andadjustments made as additional information is received (e.g., next itemsvisible, etc.). In some cases, the system is configured to stage (e.g.,set aside, within reach) items determined less likely to be suitable tobe stacked in a layer or container the system is currently building,such as a lower (or higher) layer in a pallet or container, etc. Oncemore information is known about next items to be picked/placed, astrategy that takes into account the staged (buffered) items and nextitems is generated and implemented.

FIG. 1 is a diagram illustrating an embodiment of a robotic system topalletize and/or depalletize heterogeneous items. In the example shown,system 100 includes a robotic arm 102. In this example the robotic arm102 is stationary, but in various alternative embodiments robotic arm102 may be a fully or partly mobile, e.g., mounted on a rail, fullymobile on a motorized chassis, etc. As shown, robotic arm 102 is used topick arbitrary and/or dissimilar items from a conveyor belt (or othersource) 104 and stack them on a pallet or other receptacle 106. In theexample shown, receptacle 106 comprise a pallet or base with wheels atthe four corners and at least partially closed on three of four sides,sometimes referred to as a three-sided “roll pallet”, “roll cage”,and/or “roll” or “cage” “trolley”. In other embodiments, a roll ornon-wheeled pallet with more, fewer, and/or no sides may be used. Insome embodiments, other robots not shown in FIG. 1 may be used to pushreceptacle 106 into position to be loaded/unloaded and/or into a truckor other destination to be transported, etc.

In the example shown, robotic arm 102 is equipped with a suction-typeend effector 108. End effector 108 has a plurality of suction cups 110.Robotic arm 102 is used to position the suction cups 110 of end effector108 over an item to be picked up, as shown, and a vacuum source providessuction to grasp the item, lift it from conveyor 104, and place it at adestination location on receptacle 106.

In various embodiments, one or more of 3D or other camera 112 mounted onend effector 108 and cameras 114, 116 mounted in a space in whichrobotic system 100 is deployed are used to generate image data used toidentify items on conveyor 104 and/or determine a plan to grasp,pick/place, and stack the items on receptacle 106. In variousembodiments, additional sensors not shown, e.g., weight or force sensorsembodied in and/or adjacent to conveyor 104 and/or robotic arm 102,force sensors in the x-y plane and/or z-direction (vertical direction)of suction cups 110, etc. may be used to identify, determine attributesof, grasp, pick up, move through a determined trajectory, and/or placein a destination location on or in receptacle 106 items on conveyor 104and/or other sources and/or staging areas in which items may be locatedand/or relocated, e.g., by system 100.

In the example shown, camera 112 is mounted on the side of the body ofend effector 108, but in some embodiments camera 112 and/or additionalcameras may be mounted in other locations, such as on the underside ofthe body of end effector 108, e.g., pointed downward from a positionbetween suction cups 110, or on segments or other structures of roboticarm 102, or other locations. In various embodiments, cameras such as112, 114, and 116 may be used to read text, logos, photos, drawings,images, markings, barcodes, QR codes, or other encoded and/or graphicalinformation or content visible on and/or comprising items on conveyor104.

Referring further to FIG. 1, in the example shown system 100 includes acontrol computer 118 configured to communicate, in this example viawireless communication (but in one or both of wired and wirelesscommunication in various embodiments) with elements such as robotic arm102, conveyor 104, effector 108, and sensors, such as camera 112, 114,and 116 and/or weight, force, and/or other sensors not shown in FIG. 1.In various embodiments, control computer 118 is configured to use inputfrom sensors, such as camera 112, 114, and 116 and/or weight, force,and/or other sensors not shown in FIG. 1, to view, identify, anddetermine one or more attributes of items to be loaded into and/orunloaded from receptacle 106. In various embodiments, control computer118 uses item model data in a library stored on and/or accessible tocontrol computer 118 to identify an item and/or its attributes, e.g.,based on image and/or other sensor data. Control computer 118 uses amodel corresponding to an item to determine and implement a plan tostack the item, along with other items, in/on a destination, such asreceptacle 106. In various embodiments, the item attributes and/or modelare used to determine a strategy to grasp, move, and place an item in adestination location, e.g., a determined location at which the item isdetermined to be placed as part of a planning/replanning process tostack items in/on the receptacle 106.

In the example shown, control computer 118 is connected to an “ondemand” teleoperation device 122. In some embodiments, if controlcomputer 118 cannot proceed in a fully automated mode, for example, astrategy to grasp, move, and place an item cannot be determined and/orfails in a manner such that control computer 118 does not have astrategy to complete picking and placing the item in a fully automatedmode, then control computer 118 prompts a human user 124 to intervene,e.g., by using teleoperation device 122 to operate the robotic arm 102and/or end effector 108 to grasp, move, and place the item.

FIG. 2 is a flow chart illustrating an embodiment of a process topalletize and/or depalletize. In various embodiments, the process 200 ofFIG. 2 is performed by a control computer comprising a robotic system topalletize and/or depalletize heterogeneous items, such as controlcomputer 118 of FIG. 1. In the example shown, at 202 data indicating ahigh level objective to pick and place a set of dissimilar items (i.e.,the set includes two or more different types of item) to/from areceptacle, such as a pallet, is received. For example, a controlcomputer such as control computer 118 of FIG. 1 may receive an input toload items arriving on conveyor 104 onto receptacle 106. In someembodiments, an input indicating explicitly the items expected and/ordesired to be loaded, such as an invoice or manifest, may be received.

At 204, planning (or re-planning) is performed to generate a plan topick/place items based on the high level objective received at 202 andavailable sensor information. For example, in the example shown in FIG.1, 3D or other image data generated by one or more of cameras 112, 114,and 116 may be used, along with sensor data from sensors not shown inFIG. 1 (e.g., weight sensors) to identify and determine a plan to pick,place, and stack on receptacle 106 items arriving via conveyor 104.

In some embodiments, the system may make a plan based at least in parton an invoice or other list. A plan to stack items may be generatedbased on item size, weight, density, weight distribution, rigidity,capacity of the item and/or its box or other packaging to support weightstacked on top of the item, etc. The control computer in someembodiments controls the order in which items arrive at the loadinglocation, such as via conveyor 104. In some embodiments, items mayarrive in an order not known a priori and/or the list of items may notbe known. In some such embodiments, cameras (e.g., 112, 114, 116) and/orother sensors are used to identify items and/or their attributes,generate/update a plan to stack items on the pallet or other receptacle(e.g., receptacle 106), and/or to determine strategies to grasp, pickup, move, and place each item in its corresponding place according tothe stacking plan determined at 204.

At 206, items are picked (e.g., grasped from conveyor 104, in theexample shown in FIG. 1), moved through a (predetermined/planned)trajectory to a location near where the item is to be placed, and placedat the destination according to the plan determined and/or updated at204.

In various embodiments, a packing plan may include planned use ofstackable trays, crates, boxes, bins, or other containers to groupsmaller and/or irregularly-shaped items and pack as a more readilystacked unit. In some embodiments, in a depalletizing operation arobotic system as disclosed herein is configured to recognize suchcontainers, open them if needed, and empty them by removing individualitems. Once emptied, the container is moved to a staging location, fromwhich in various embodiments other robots and/or human workers mayretrieve and move them out of the way.

In the example shown, (re-)planning and plan implementation (204, 206)continue until the high level objective (202) is completed (208), atwhich the process 200 ends. In various embodiments, re-planning (204)may be triggered by conditions such as arrival of an items that is notexpected and/or cannot be identified, a sensor reading indicating anattribute has a value other than what was expected based on itemidentification and/or associated item model information, etc. Otherexamples of unexpected conditions include, without limitation,determining that an expected item is missing, reevaluating itemidentification and determining an item is other than as originallyidentified, detecting an item weight or other attribute inconsistentwith the item as identified, dropping or needing to re-grasp the item,determining that a later-arriving item is too heavy to be stacked on oneor more other items as contemplated by the original and/or current plan,and detecting instability in the set of items as stacked on thereceptacle.

FIG. 3A is a diagram illustrating an example of a pallet as assembled inan embodiment of a robotic system to palletize and/or depalletizeheterogeneous items. In various embodiments, the items shown in FIG. 3Aas stacked on pallet 302 may have been stacked by a robotic system, suchas system 100 of FIG. 1, according to a computer-determined (and/orupdated) plan, as in the process 200 of FIG. 2.

In the example shown, items 304, 306, and 308 are stacked in a first(bottom-most) layer, with items 310, 312, 314, and 316 stacked in alayer on top of items 304, 306, and 308. Items 318 and 320 have beenstacked on top.

In various embodiments, the items as shown may have been selected anddesignated to be placed as shown in FIG. 3A based on item identificationand/or attributes, e.g., as determined and/or measured based on image,weight, and/or other sensor data. For example, items 304, 306, and 308may have been placed in the bottom layer, as shown, based on their(relative) size, weight, rigidity, strength, and/or other indication ofability (of item and/or packaging) to support weight stacked on top.

The placement, orientation, etc., of each item is determined in variousembodiments by applying one or more heuristics, algorithms, or othertools and/or techniques. For example, each item is added at least inpart by determining a location within the entire pallet and/or in alayer currently being stacked and/or arranged so as to provide arelatively (more) flat top surface to form the basis for a next layer,to minimize stack surface area, to minimize current layer perimeter,etc. In various embodiments, one or more of the following heuristics maybe used: place next item to maintain center of mass close to the centerof the pallet; maintain a clear motion path for the robotic arm forsubsequent items; maintain visibility of the cameras to all top layerboxes or unboxed items; etc.

In the example shown, items 310 and 312 are round (at least incross-section), and in some embodiments their shape would be taken intoconsideration in selecting a location to place them. For example, aninterior location in which items 310 and 312 are constrained by otheradjacent items, as shown in FIG. 3A, may be selected over a location onthe edge or end of a layer.

Items 318 and 320 may have been selected to be placed on top, in variousembodiments, based on one or more attributes, such as their weight (ordensity), ability to support weight stacked on top of them, etc.

FIG. 3B is a diagram illustrating an example of a pallet as assembled inan embodiment of a robotic system to palletize and/or depalletizeheterogeneous items. In this example, the items 314 included in theexample as shown in FIG. 3A are omitted. In various embodiments, a planto stack items as shown in FIG. 3B may be generated by a controlcomputer, such as control computer 118 of FIG. 1. In the example shown,an algorithmically generated plan may consider the item 318 stacked onitems 310, 312 to be in a stable position. For example, there may be nocomputed net moment about any point or axis that would lead a controlcomputer to determine the position as shown is not stable. In practice,however, slight deviation from plan in the placement of items on pallet302 could result in the item 318 not being in a stable position oncestacked on items 310, 312, for example. In some embodiments, a roboticarm and end effector (e.g., suction, gripper) used to place item 318includes sensors and logic to detect instability. For example, imagedata may be processed and may indicate the items is leaning to theright, as shown. Or, prior to releasing the item 318 force sensors inthe end effector may be used to detect the item would not be stable ifreleased. For example, if the item gives way (or provides resistanceunevenly) when pressed down but not yet released, in various embodimentsthe system determines the item is not stable.

In various embodiments, detection of unexpected instability triggersresponsive action. For example, other items (e.g., items 314 as in FIG.3A) may be placed in position to further support the item that was notstable. Or, the item may be shifted to a different nearby position(e.g., snugged left in the example shown), to see if the item would bestable in the new position. In various embodiments, if automatedprocessing fails to determine a resolution, human intervention (e.g.,via teleoperation, manually, etc.) may be triggered.

FIG. 4A is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items. In the example shown, a location 402has been determined to place an item (not shown in FIG. 4A) on a pallet404 in a position adjacent to previously-placed items 406. For example,the location 402 may have been determined based on item attributes(size, weight, dimensions, etc.), as in 204 of FIG. 2.

FIG. 4B is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items. In the example shown, the item 408 hasbeen moved to a first order of accuracy to a position at or near theintended location 402. In this example, the item 408 has a slightlyirregular outer texture, which may have resulted in imperfect pickingand/or placement of the item at or near location 402. In someembodiments, variations and/or inconsistencies in the image data and/orother sensor data received concerning one or more of location 402,pallet 404, items 406, and item 408 may have resulted in the imperfectplacement of item 408 as shown in FIG. 4B.

FIG. 4C is a diagram illustrating an example of item placement on apallet in an embodiment of a robotic system to palletize and/ordepalletize heterogeneous items. In the example shown, the item 408 hasbeen snugged into a location at or near planned location 402 to a secondorder of accuracy. In various embodiments, second order sensors, such asforce sensors embodied in/on a robotic arm and/or end effector, are usedto snug an item into a more tightly-fitted position in a stack. Forexample, the robotic arm may be used to snug an item, such as item 408,to a position more close and in (more complete) contact with adjacentitems (e.g., items 406) and/or other structures, such as side walls (inthe case of a pallet or trolley that has one or more side walls, forexample).

In some embodiments, an item such as item 408 may be snugged moreclosely to adjacent items, sidewalls, etc. and may result in the itemending up in a location not entirely consistent with the original plan.In some embodiments, snug placement is desired and preferred withinlayers to maximize the use of available space and to maximize stabilityby couple adjacent items to one another and (as applicable) to adjacentstructures, such as sidewalls.

FIG. 5 is a flow chart illustrating an embodiment of a process to placean item on a pallet. In various embodiments, the process 500 isperformed by a control computer, such as control computer 118 of FIG. 1,e.g., to pick and place successive items according to a plan.

In the example shown, at 502 a placement location and trajectory toreach the location are determined for a next item to be picked andplaced. For example, a predetermined (e.g., according to a plan) and/ora dynamically-determined (e.g., based on item identification, sensordata, locations of previously-placed items, etc.) location and atrajectory to move the item to that location may be determined. At 504,one or more first order sensors (e.g., image data) are used to move theitem to the destination location with first order accuracy (e.g., as inthe example shown in FIG. 4B). At 506, one or more second order sensors(e.g., force sensors) are used to snug the item into place (as in FIG.4C) and/or to detect instability (as in FIG. 3B) or other unexpectedconditions. If the item is determined at 508 to have been placedsuccessfully, at 510 processing continues with a next item, with respectto which a next iteration of the process 500 is performed. If the itemis determined at 508 not to have been placed successfully (e.g., itemnot in expected location as determined by image data, item not stable,stack not stable, etc.), then at 512 the system tries again viaautomated processing to securely place the item, e.g., if a furtherstrategy to attempt to do so is available, and/or human intervention isinvoked (e.g., via on demand teleoperation) until the item is determinedat 508 to have been placed successfully.

In various embodiments, a robotic system to palletize/depalletize asdisclosed herein is configured to make and/or update dynamically a planto place and stack items. For example, the next item(s) to be placed maynot be known ahead of time, and adjustments to the plan may be required.For example, if soft and/or fragile items have been placed or planned tobe placed in a lower layer and heavier items are detected to be arriving(next), the plan may be adjusted dynamically to buffer (set aside in astaging area) the soft and/or fragile items, allowing the heavier itemsto be placed in the lower level first. In some embodiments, if the softand/or fragile items have already been placed (e.g. on the pallet), theitems may be removed to the buffer (staging) area to allow the heavieritems to be placed in the lower level in their place.

FIG. 6 is a flow chart illustrating an embodiment of a process to placeitems on a pallet. In various embodiments, the process 600 is performedby a control computer, such as control computer 118 of FIG. 1, based onsensor data, such as image data generated by cameras 112, 114, and 116and/or weight or other sensors. In the example shown, at 602 dataregarding the next N items to be added to the pallet or other receptacleis received. For example, image, weight, shape, item identification,invoice, optical or other scanner output, and/or other data may bereceived.

At 604, a plan to pick, move, and pack/stack items is made and/orupdated based on the data received at 602 and/or previously-determineddata. For example, the plan may be made and/or updated at 604 based onattributes of the next N items, attributes of M (zero or more) items ina buffer (staging) area, and items P (zero or more) already placed andcurrently arranged on the pallet. For example, if the N items includeitems that are heavier than the M items in the buffer but lighter thanthe P items already on the pallet, the plan may be updated to place theN items next followed (tentatively) by the M items in the buffer.Conversely, if the N items were larger and heavier than both the M itemsin the buffer and the P items already on the pallet, the P items may beremoved to the buffer and the N item placed in the bottom layer.

At 606, items are picked (e.g., from the conveyor or other source, thebuffer or staging area, and/or the pallet, as required) to implement theplan. If implementation of the plan results in the high level objectivebeing determined at 608 to have been completed (e.g., all items placed),the process ends. Otherwise, receipt of data, updating the plan, andpicking/placing according to the plan (602, 604, 606) continue untildone (608).

FIG. 7 is a flow chart illustrating an embodiment of a process to placean item on a pallet. In various embodiments, the process 700 isperformed by a control computer, such as control computer 118 of FIG. 1,based on sensor data, such as image data generated by cameras 112, 114,and 116. In various embodiments, the process 700 is used to determineitem attributes and placement dynamically, e.g., based on item size andshape as determined based at least in part on image data.

In the example shown, at 702 image data is received and processed. Forexample, image data from two or more cameras may be received and mergedto generate a composite set of points in three dimensional space.

At 704, image data received and processed at 702 is fitted to a model.For example, the composite set of points may be compared tocorresponding data comprising a library of item models and a “best fit”or “best match” model may be determined.

At 706, gaps in the image data are filled, e.g., using data from thebest fit model, and a bounding container (e.g., a bounding “box” for anitem that is rectangular or nearly so in all dimensions) is determinedfor the item.

Optionally, at 708, a graphical depiction of the bounding container issuperimposed on the composite and/or raw image of the item (e.g., rawvideo) to provide a composite display, e.g., to a human user monitoringthe operation.

At 710, the model determined at 704 and the bounding containerdetermined at 706 are used to determine a destination location at whichto place the item (e.g., on a pallet). In some embodiments, the boundingcontainer may be displayed in the determined placement location, e.g.,in a visual display and/or representation of the operation to pick andplace the item.

FIG. 8 is a flow chart illustrating an embodiment of a process todetermine a strategy to place an item on a pallet. In variousembodiments, the process 800 is performed by a control computer, such ascontrol computer 118 of FIG. 1. In the example shown, at 802 item datais received and processed. Example of item data include withoutlimitation image data, optical or other scanner output, item weight,etc.

At 804, an attempt is made to match the item data to a model, e.g., amodel comprising a library of item models. For example, markings on theitem or its packaging may be read to identify the item and look up anitem-specific model. In some embodiments, sensor readings may point indifferent directions. In some embodiments, outlier and/or randomlyselected sensor data may be removed from consideration to determinewhether the remaining data can be match (e.g., with a prescribed degreeof confidence) to an item model. If so, the sensor data from that sensoris discarded and a match is determined at 806 to have been found.

If a match to an item-specific model is determined at 806 to have beenfound, based on the data received at 802, a strategy based on that modelis used at 808 to determine a strategy to grasp, pick up, move, and/orplace the item.

If it is determined at 806 that a match to an item-specific model cannotbe found, at 810 the size, weight, shape, and/or other attributes of theitem are determined and attempted to be matched to a model associatedwith items of that size, weight, shape, etc. If at 812 it is determinethat a match to such a model has been found, at 814 the determined modelis used to determine a strategy to grasp, pick up, move, and/or placethe item. Otherwise, at 816 human intervention is triggered, e.g., tomanually input a strategy and/or an item identification to be mapped toa strategy, and/or to complete all or part of the operation with respectto the item by teleoperation, manually, etc.

At 818 it is determined whether processing the item has generatedadditional or new information about the item and/or item type. If so, at820 the new information is used to generate and/or update a model forthe item and/or item type. If no new information has been developedand/or once such information has been reflected in any applicablemodel(s), the process ends.

In some embodiments, detection of a new item or item type triggers adiscovery process in which attributes of the item may be determined anddetermined more fully or precisely. For example, the system may use therobotic arm to pick up the item and hold it in different locationsand/or at different orientations to generate (additional) images orviews of the item to be used to model or more completely model the item.

In various embodiments, a robotic system to palletize and/or depalletizeheterogeneous items as disclosed herein includes a robotic arm with asuction-based, gripper-type, or other end effector at the operative end.The end effector in various embodiments may include on or more sensorsused to palletize and/or depalletize heterogeneous items as disclosedherein. For example, the end effector may include image sensors used tofacilitate first order picking and placing of an item and one or moreforce sensors to facilitate snugging items into place, e.g., adjacent toother items, sidewalls, and/or other structures. In another example, insome embodiments a suction-based end effectuator may include airpressure sensors to detect when a secure grasp has been made with theobject surface and/or to detect when the object is losing contact orshearing off.

FIG. 9 is a block diagram illustrating an embodiment of a suction-basedend effector in a robotic system to palletize and/or depalletizeheterogeneous items. In various embodiments, robotic arm end effector900 of FIG. 9 may be used to implement end effector 108 of FIG. 1.

In the example shown, end effector 900 includes a body or housing 902attached to robotic arm 904 via a rotatable coupling. In someembodiments, the connection between housing 902 and robotic arm 904 maycomprise a motorized joint controlled by a control computer, such ascontrol computer 118 of FIG. 1. End effector 900 further includes asuction or other pneumatic line 906 that runs through the robotic arm904 into the housing 902 to supply a vacuum source to suction controlmodule 908. In various embodiments, control module 908 is connected,e.g., via wireless and/or wired communication through communicationinterface 914 to a control computer external to end effector 900, e.g.,control computer 118 of FIG. 1. The control module 908 includeselectronic and/or electro-mechanical elements operable to supply suctionforce to suction cups 910, 912 comprising the end effector 900, e.g., toattach the end effector through suction to an item to be picked up,moved, and placed using end effector 900.

In the example shown, a camera 916 mounted on the side of housing 902provides image data of a field of view below the end effector 900. Aplurality of force sensors 918, 920, 922, 924, 926, and 928 measureforce applied to the suction cups 910 and 912, respectively. In variousembodiments, the force measurements are communicated via communicationinterface 914 to an external and/or remote control computer. The sensorreadings are used in various embodiments to enable the robotic arm 904and end effector 900 to be used to snug an item into place adjacent toother items and/or sidewalls or other structures, and/or to detectinstability (e.g., insufficient push back with the item is pressed downupon while still under suction but in the place in which the item wasexpected to be placed and to be stable). In various embodiments, thehorizontally mounted pairs of force sensors (e.g., 918 and 922, 924 and928) are placed at right angles in the x-y plane, to enable force to bedetermined in all horizontal directions.

FIG. 10 is a block diagram illustrating an embodiment of a gripper-styleend effector in a robotic system to palletize and/or depalletizeheterogeneous items. In various embodiments, robotic arm end effector1000 of FIG. 10 may be used to implement end effector 108 of FIG. 1.

In the example shown, end effector 1000 includes a body or housing 1002attached to robotic arm 1004 via a rotatable coupling. In someembodiments, the connection between housing 1002 and robotic arm 1004may comprise a motorized joint controlled by a control computer, such ascontrol computer 118 of FIG. 1. End effector 1000 further includes agripper comprising articulating digits 1010 and 1012 and a power line1006 that runs through the robotic arm 1004 into the housing 1002 tosupply electrical power to gripper control module 1008. In variousembodiments, control module 1008 is connected, e.g., via wireless and/orwired communication through communication interface 1014 to a controlcomputer external to end effector 1000, e.g., control computer 118 ofFIG. 1. The control module 1008 includes electronic and/orelectro-mechanical elements operable to manipulate the gripper digits1010, 1012, e.g., to grasp an item to be picked up, moved, and placedusing end effector 1000.

In the example shown, a camera 1016 mounted on the side of housing 1002provides image data of a field of view below the end effector 1000. Aplurality of force sensors 1018, 1020, 1022, 1024, 1026, and 1028measure force applied to the mount points of digits 1010 and 1012,respectively. In various embodiments, the force measurements arecommunicated via communication interface 1014 to an external and/orremote control computer. The sensor readings are used in variousembodiments to enable the robotic arm 1004 and end effector 1000 to beused to snug an item into place adjacent to other items and/or sidewallsor other structures, and/or to detect instability (e.g., insufficientpush back with the item is pressed down upon while still under suctionbut in the place in which the item was expected to be placed and to bestable).

While a suction-type effector is shown in FIG. 9 and a gripper-typeeffector is shown in FIG. 10, in various embodiments one or more otherand/or different types of end effector may be used in a robotic systemto palletize/depalletize heterogeneous items, as disclosed herein.

In various embodiments, a plan to pick and stack items on a pallet orother receptacle and/or to depalletize items takes into considerationthe trajectory through which each item is to be moved by the roboticarm. For example, a large box needs more clearance than a small item.Also, a later-placed item must be moved through a trajectory that avoidscollisions with previously-placed items, etc.

In some embodiments, sensors are used to detect collisions with otheritems, the receptacle, and/or the environment, and to continue automatedoperation by “compliant” adjustment of the trajectory. For example, if awall or other structure is bumped into, in some embodiments, the roboticarm reduces force and adjusts the trajectory to follow along theobstacle until it is clear of it.

FIG. 11 is a flow chart illustrating an embodiment of a process tocompliantly move an item to a destination location. In variousembodiments, the process 1100 of FIG. 11 may be implemented by a controlcomputer, such as control computer 118 of FIG. 1, based at least in parton sensor readings from sensors comprising and/or otherwise associatedwith a robotic arm, end effector, and/or environment.

In the example shown, at 1102 an items is grasped and the items is begunto be moved along a planned trajectory. If an unexpected or thresholdsensor value is received (1104), one or both of the grasp and/or thetrajectory may be adjusted (1106). For example, if the item is detectedto be slipping out of the original grasp, the robotic arm may be used toset the item down and re-grasp it more securely. If the item isdetermined (e.g., based on force sensors, image data, both, etc.) tohave bumped up against an obstacle, the trajectory may be adjusted tomove the item along and/or around the obstacle. For example, theoriginally planned trajectory may be deviated from so as to maintain aforce sensor reading below a threshold. Once no opposing force isdetected, the original trajectory may be resume, if possible, and/or anew next segment trajectory may be computed and followed.

If no (further) unexpected sensor value is encountered (1104), the itemscontinues along its trajectory to its destination (1108).

FIG. 12 is a flow chart illustrating an embodiment of a process tochange a robotic arm grasp of an item. In various embodiments, theprocess 1200 of FIG. 12 may be implemented by a control computer, suchas control computer 118 of FIG. 1, based at least in part on sensorreadings from sensors comprising and/or otherwise associated with arobotic arm, end effector, and/or environment. At 1202, the system(e.g., control computer, based on force/torque sensors and/or otherdata) detects the item has not been grasped at or near its center ofgravity. For example, force and/or torque sensors may detect a greaterthan threshold torque about a connection point at which the end effectoris attached to the robotic arm. Or, image data may indicate the item isnot in a mostly level position while in the grasp of the robotic arm andend effector. At 1204, the item is set down (e.g., in a buffer orstaging area, or in the location from which it was picked up, etc.) andthe grasp is adjusted (e.g., the item is released and re-grasped at adifferent location). At 1206, the system resumes moving the item along atrajectory to the destination placement location.

FIG. 13 is a flow chart illustrating an embodiment of a process to use arobotic arm to determine an attribute of an item to be stacked on apallet. In various embodiments, the process 1300 of FIG. 13 may beimplemented by a control computer, such as control computer 118 ofFIG. 1. In the example shown, a robotic arm and/or end effector is usedto probe an item to determine one or more attributes of the item, forexample how soft, heavy, dense, etc., the item is. For example, if theitem gives to light pressure it is determined to be soft. If the item islight for its size, as determined based on image data, the item isdetermined to be less dense. If at 1304 the item has been determined tobe soft and/or less dense, the item is buffered at 1306, if needed, tobe placed in an upper layer of the stack. If the item is not determinedat 1304 to be soft or less dense, the item is added to the stack withoutbuffering at 1306.

FIG. 14 is a flow chart illustrating an embodiment of a process to use arobotic arm to detect and respond to instability in a set of items asstacked on a pallet. In various embodiments, the process 1400 of FIG. 14may be implemented by a control computer, such as control computer 118of FIG. 1. In various embodiments, the process 1400 is used to detectstack instability, e.g., based on internal (e.g., force) and/or external(e.g., image) sensor data.

In the example shown, stack stability is monitored at 1402. For example,cameras in the environment may be used to detect that the stack hasstarted to lean, crumble, slide, etc. Or, force sensors on the roboticarm and/or end effector may be used to detect that the stack or aportion thereof has become unstable. If at 1404 it is determined thestack has become unstable, at 1406 the partial stack is automaticallywrapped, e.g. in plastic, to prevent slippage and reinforce thestability and structure of the stack. If the stack is not determined(again) at 1404 to have become unstable, and in any event once theentire stack (e.g., pallet or other receptacle) has been completed(1408), the stack and/or remaining unwrapped portion thereof is wrappedautomatically at 1410 and the process ends.

In various embodiments, techniques disclosed herein enable arbitrarygroups of heterogeneous items to be palletized and/or depalletize by arobotic system, without or with minimal human intervention.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A robotic system, comprising: a communicationinterface; and a processor coupled to the communication interface andconfigured to: receive via the communication interface data associatedwith a plurality of items to be stacked on or in a destination location,wherein the plurality of items comprises a first plurality of items;generate based at least in part on the received data a plan to stack theitems on or in the destination location; implement the plan at least inpart by controlling a robotic arm to pick up the items and stack them onor in the destination location according to the plan, including by foreach item: using one or more first order sensors to move the item to afirst approximation of a destination position for that item at thedestination location; and using one or more second order sensors to snugthe item into a final position; receive via the communication interfacedata associated with a second plurality of items; use the dataassociated with the first plurality of items and the data associatedwith the second plurality of items to determine dynamically an adjustedplan to stack items comprising the first and second plurality of itemson or in the destination location; and implement the adjusted plan,wherein to implement the adjusted plan, the processor is configured to:remove from a first location of the destination location one or moreitems of the first plurality of items that have been placed on or in thedestination location: place the one or more removed items in a bufferarea; and place at the first location of the destination location one ormore items of the second plurality of items.
 2. The system of claim 1,wherein the destination location comprises a pallet, box, or otherreceptacle.
 3. The system of claim 1, wherein the data associated withthe plurality of items includes sensor data for at least a subset of theitems.
 4. The system of claim 3, wherein the sensor data includes one ormore of image data, weight data, force data, size data, dimension data.5. The system of claim 1, wherein the data associated with the pluralityof items includes one or both of item and item type data for at least asubset of the items.
 6. The system of claim 5, wherein the one or bothof item and item type data includes optical or other scanner output. 7.The system of claim 1, wherein the processor is configured to generatethe plan at least in part by determining for each of at least a subsetof items a corresponding model for the item and to use the model todetermine the plan with respect to the item.
 8. The system of claim 7,wherein the processor is further configured to detect a new item type oritem attribute and to generate and store data updating a library of itemmodels to reflect the new item type or item attribute.
 9. The system ofclaim 1, wherein the processor is further configured to use the roboticarm to determine an attribute of at least a subset of the items.
 10. Thesystem of claim 1, wherein the processor is further configured to detectinstability with respect to one or more items and to adjust one or bothof the plan and the placement of one or more items to make the resultingstack of items more stable.
 11. The system of claim 1, wherein theprocessor is further configured to detect instability with respect to apartial stack comprising a subset of the plurality of items and to wrapthe partial stack automatically in response to detecting theinstability.
 12. The system of claim 1, wherein the processor is furtherconfigured to detect that an item being moved to the destinationlocation on or in a receptacle has encountered an obstacle and to adjusta trajectory of the item to go along or around the obstacle.
 13. Amethod to control a robot, comprising: receiving via a communicationinterface data associated with a plurality of items to be stacked on orin a destination location, wherein the plurality of items comprises afirst plurality of items; using a processor generate based at least inpart on the received data a plan to stack the items on or in thedestination location; using the processor to implement the plan at leastin part by controlling a robotic arm of the robot to pick up the itemsand stack them on or in the destination location according to the plan,including by for each item: using one or more first order sensors tomove the item to a first approximation of a destination position forthat item at the destination location; and using one or more secondorder sensors to snug the item into a final position; receiving dataassociated with a second plurality of items; using the data associatedwith the first plurality of items and the data associated with thesecond plurality of items to determine dynamically an adjusted plan tostack items comprising the first and second plurality of items on or inthe destination location; and implementing the adjusted plan, whereinimplementing the adjusted plan comprises: removing from a first locationof the destination location one or more items of the first plurality ofitems that have been placed on or in the destination location; placingthe one or more removed items in a buffer area; and placing at the firstlocation of the destination location one or more items of the secondplurality of items.
 14. The method of claim 13, wherein the destinationlocation comprises a pallet, box, or other receptacle.
 15. The method ofclaim 13, wherein the data associated with the plurality of itemsincludes sensor data for at least a subset of the items.
 16. The methodof claim 15, wherein the sensor data includes one or more of image data,weight data, force data, size data, dimension data.
 17. The method ofclaim 13, wherein the data associated with the plurality of itemsincludes one or both of item and item type data for at least a subset ofthe items.
 18. The method of claim 13, wherein the processor isconfigured to generate the plan at least in part by determining for eachof at least a subset of items a corresponding model for the item and touse the model to determine the plan with respect to the item.
 19. Themethod of claim 13, detecting instability with respect to a partialstack comprising a subset of the plurality of items and to wrap thepartial stack automatically in response to detecting the instability.20. A computer program product to control a robot, the computer programproduct being embodied in a non-transitory computer readable medium andcomprising computer instructions for: receiving data associated with aplurality of items to be stacked on or in a destination location,wherein the plurality of items comprises a first plurality of items;generating based at least in part on the received data a plan to stackthe items on or in the destination location; implementing the plan atleast in part by controlling a robotic arm of the robot to pick up theitems and stack them on or in the receptacle according to the plan,including by for each item: using one or more first order sensors tomove the item to a first approximation of a destination position forthat item at the destination location; and using one or more secondorder sensors to snug the item into a final position; receiving dataassociated with a second plurality of items; using the data associatedwith the first plurality of items and the data associated with thesecond plurality of items to determine dynamically an adjusted plan tostack items comprising the first and second plurality of items on or inthe destination location; and implementing the adjusted plan, whereinimplementing the adjusted plan comprises: removing from a first locationof the destination location one or more items of the first plurality ofitems that have been placed on or in the destination location; placingthe one or more removed items in a buffer area; and placing at the firstlocation of the destination location one or more items of the secondplurality of items.